title: A prediction model to identify patients at high risk for 30-day readmission after percutaneous coronary intervention.
abstract: BACKGROUND
  The Affordable Care Act creates financial incentives for hospitals to minimize readmissions shortly after discharge for several conditions, with percutaneous coronary intervention (PCI) to be a target in 2015. We aimed to develop and validate prediction models to assist clinicians and hospitals in identifying patients at highest risk for 30-day readmission after PCI.
  
  
  RESULTS
  We identified all readmissions within 30 days of discharge after PCI in nonfederal hospitals in Massachusetts between October 1, 2005, and September 30, 2008. Within a two-thirds random sample (Developmental cohort), we developed 2 parsimonious multivariable models to predict all-cause 30-day readmission, the first incorporating only variables known before cardiac catheterization (pre-PCI model), and the second incorporating variables known at discharge (Discharge model). Models were validated within the remaining one-third sample (Validation cohort), and model discrimination and calibration were assessed. Of 36,060 PCI patients surviving to discharge, 3760 (10.4%) patients were readmitted within 30 days. Significant pre-PCI predictors of readmission included age, female sex, Medicare or State insurance, congestive heart failure, and chronic kidney disease. Post-PCI predictors of readmission included lack of β-blocker prescription at discharge, post-PCI vascular or bleeding complications, and extended length of stay. Discrimination of the pre-PCI model (C-statistic=0.68) was modestly improved by the addition of post-PCI variables in the Discharge model (C-statistic=0.69; integrated discrimination improvement, 0.009; P<0.001).
  
  
  CONCLUSIONS
  These prediction models can be used to identify patients at high risk for readmission after PCI and to target high-risk patients for interventions to prevent readmission.
authors.affiliation: Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Wasfy
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rosenfield
authors.forename: Katya
authors.initials: K
authors.lastname: Zelevinsky
authors.forename: Rahul
authors.initials: R
authors.lastname: Sakhuja
authors.forename: Ann
authors.initials: A
authors.lastname: Lovett
authors.forename: John A
authors.initials: JA
authors.lastname: Spertus
authors.forename: Neil J
authors.initials: NJ
authors.lastname: Wimmer
authors.forename: Laura
authors.initials: L
authors.lastname: Mauri
authors.forename: Sharon-Lise T
authors.initials: SL
authors.lastname: Normand
authors.forename: Robert W
authors.initials: RW
authors.lastname: Yeh
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008404:Massachusetts; D008875:Middle Aged; D015999:Multivariate Analysis; D010359:Patient Readmission; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects*; D012042:Registries; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome
keywords: outcomes research; percutaneous coronary intervention; performance measures